The proposed project will develop an optimal methodology for non-invasive estimation and localization of real-time neural activity generated within multiple intracranial sources. This methodology will integrate EEG/ERP inverse methods with anatomical information from magnetic resonance imaging (MRI) to construct optimal head models and to constrain possible source geometries. The commercial aim is to develop modular software that supports a variety of source and head models for the analysis of surface- recorded electrophysiologic data. Immediate applications are envisioned in basic and clinical research ranging from functional neuroanatomy to cognitive psychophysiology. This user research will likely produce primary clinical applications in neurosurgery, neurology, psychiatry and neuropsychology. Phase I consists of a volume conductor study followed by an inverse operator study. Two new methods are derived for fast computation of volume conduction transfer coefficients using head geometry data: source- to-scalp geometric fitting, and transform to the unbounded volume. Transfer coefficient errors will be measured using the boundary element method as a standard. Applying different methods to simulated data constructed from actual MRI and EEG/ERP data, the inverse operator study will reveal the effects of measurement errors produced by coherently colored background EEG, and interactions with approximation errors produced by simplified head models.